google-research/scann at master · google-research/google-research
ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale. This code implements [1, 2], which includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX support. ScaNN achieves state-of-the-art performance on an
Using PyTorch + NumPy? You're making a mistake.
Update: The problem is now fixed in Pytorch but can still happen in tensorflow-keras. Discussion on reddit. Bugs in ML code are notoriously hard to fix - they don’t cause compile errors but silently regress accuracy. Once you have endured the pain and fixed one of these, the lesson is forever etched into your brain, right? Wrong. Recently, an old foe made a comeback - a familiar bug bit me again!
GitHub - google/tangent: Source-to-Source Debuggable Derivatives in Pure Python
Tangent is a new, free, and open-source Python library for automatic differentiation. Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself,
Intel® Optimization for TensorFlow* Installation Guide
TensorFlow* is a widely-used machine learning framework in the deep learning arena, demanding efficient utilization of computational resources. In order to take full advantage of Intel® architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning application
Home — TensorFlow
Deploy ML on mobile, microcontrollers and other edge devices
Deep Learning のフレームワーク Chainer を公開しました - Preferred Networks Tech Blog
こんにちは、得居です。最近は毎晩イカになって戦場を駆けまわっています。 本日、Deep Learning の新しいフレームワークである Chainer を公開しました。 Chainer 公式サイト GitHub – pfnet/chainer Chainer Documentation Chainer は、ニューラルネットを誤差逆伝播法で学習するためのフレームワークです。以下のような特徴を持っています。 Python のライブラリとして提供（要 Python 2.7+） あらゆるニューラルネットの構造に柔軟に対応 動的な計算グラフ構築による直感的なコード GPU をサポートし、複数 GPU をつかった学習も直感的に記述可能 ニューラルネットをどのように書けるか 次のコードは多層パーセプトロンの勾配を計算する例です。 from chainer import FunctionSet, Vari
Entry Point Data
In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. Sections Installing Python packages In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you
Finding the K in K-Means Clustering
A couple of weeks ago, here at The Data Science Lab we showed how Lloyd’s algorithm can be used to cluster points using k-means with a simple python implementation. We also produced interesting visualizations of the Voronoi tessellation induced by the clustering. At the end of the post we hinted at some of the shortcomings of this clustering procedure. The basic k-means is an extremely simple and
PyStruct - Structured Learning in Python — pystruct 0.2.4 documentation
PyStruct - Structured Learning in Python¶ PyStruct aims at being an easy-to-use structured learning and prediction library. Currently it implements only max-margin methods and a perceptron, but other algorithms might follow. The learning algorithms implemented in PyStruct have various names, which are often used loosely or differently in different communities. Common names are conditional random f
Gensim: topic modelling for humans
✔ Train large-scale semantic NLP models ✔ Represent text as semantic vectors ✔ Find semantically related documents from gensim import corpora, models, similarities, downloader # Stream a training corpus directly from S3. corpus = corpora.MmCorpus("s3://path/to/corpus") # Train Latent Semantic Indexing with 200D vectors. lsi = models.LsiModel(corpus, num_topics=200) # Convert another corpus t
Zipline in the Cloud
Zipline in the Cloud Thomas Wiecki Source: http://www.mountainexposures.co.uk About me PhD student at Brown University. Computational Cognitive Neuroscience. Quantitative Researcher at Quantopian. Optimizing trading algorithms. Algorithmic Trading Algorithm makes trading decision automatically, based on external events (price, volume, twitter trends...) Zipline http://zipline.io/ Open-source backt
GitHub - 0joshuaolson1/lstm-g: An implementation of a generalized version of the Long Short-Term Memory neural network architecture and algorithm, one of the most powerful supervised machine learning methodologies
THIS LIBRARY IS INCORRECTLY WRITTEN AND YOU SHOULD USE https://github.com/cazala/synaptic INSTEAD (it works and is maintained). Specifically, The XOR and Distracted Sequence Recall examples (which I'm not sure are right) show that it doesn't perform as expected. Those who want to try debugging this can find each other through the public Generalized LSTM contact page (https://docs.google.com/docume
Text Classification and Feature Hashing: Sparse Matrix-Vector Multiplication with Cython | Newsle Blog
by Erich Owens Optimizing the memory footprint of a classifier used here at Newsle set us down a rabbit hole of rewriting a basic Scipy function with Cython, something that only became a problem when our high-dimensional text spaces grew to a cartoonish size, thanks to the hashing trick. Here I motivate the use of the hashing trick, how we use sparse matrix-vector multiplication for text classific
Welcome — Theano 0.9.0 documentation
Welcome¶ Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU. efficient symbolic differentiation – Theano does your d
類似楽曲検索システムを作ろう - 人工知能に関する断創録
もう1年以上かけて音声信号処理の勉強をしてきました（Pythonで音声信号処理）。ここらで具体的なアプリケーションとして類似楽曲検索の実験をしてみたのでレポートをまとめておきます。言語はPythonです。 前に 類似画像検索システムを作ろう（2009/10/3） Visual Wordsを用いた類似画像検索（2010/2/27） という画像の類似検索に関するエントリを書きましたが、今回は画像ではなく音楽を対象に類似検索をやってみたいと思います！ 今回作る類似楽曲検索システムは、従来からよくあるアーティスト名や曲名などテキストで検索するシステムや購買履歴をもとにオススメする協調フィルタリングベースのシステムとは異なります。WAVEファイルやMP3ファイルなどの音楽波形そのものを入力とするのが特徴です。たとえば、「具体的なアーティストや曲名は知らないけれど、この曲とメロディや雰囲気が似た曲がほ
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GitHub - elcorto/imagecluster: Cluster images based on image content using a pre-trained deep neural network, optional time distance scaling and hierarchical clustering.
GitHub - ibayer/fastFM: fastFM: A Library for Factorization Machines
Strategies and Tools for Parallel Machine Learning in Python
GitHub - abhik/pebl: Python Environment for Bayesian Learning
機械学習の Python との出会い — 機械学習の Python との出会い